Relevance Criteria for Spatial Information Retrieval Using Error-Tolerant Graph Matching

In this paper, we present a graph-based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image features and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows determining the bounds of error tolerance and, in this way, characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy, the derived bounds of error tolerance are essential     

Solution of inverse problems in image processing by waveletexpansion

We describe a wavelet-based approach to linear inverse problems in image processing. In this approach, both the images and the linear operator to be inverted are represented by wavelet expansions, leading to a multiresolution sparse matrix representation of the inverse problem. The constraints for a regularized solution are enforced through wavelet expansion coefficients. A unique feature of the wavelet approach is a general and consistent scheme for representing an operator in different resolutions, an important problem in multigrid/multiresolution processing. This and the sparseness of the representation induce a multigrid algorithm. The proposed approach was tested on image restoration problems and produced good results.     

一种新的基于内容的图像检索方法

针对传统图像检索的不足,提出一种新的基于内容的图像检索方法。该方法首先构造色彩距离公式,根据色彩距离公式自动判别图片的主色调,然后利用Lucene的索引模块为图片创建索引,利用搜索模块实现图像检索。通过使用24张图片对该方法的有效性进行验证,结果表明：该方法虽然简单,但是检索的准确率较高且易于实现。     

Bayesian statistics, factor analysis, and PET images I. Mathematical background

The problem of image reconstruction in positron emission tomography (PET) is examined, although the approach is quite general and may have other applications. The approach is based on the maximum-likelihood method L.A. Shepp and Y. Vardi (1982), with their assumption that the number of image pixels is greater than the number of data points. In this situation a (nonunique) solution can be written down directly, although it is not guaranteed to be positive definite. The arbitrariness in this solution can be precisely characterized by a geometric argument. A unique solution can be obtained only by introducing prior information. It is suggested that factor analysis is an efficient way to do this. In the simplest application of the method, the solution is written as the sum of two parts, r(alpha )+t(alpha), where r(alpha) is determined solely by the data and t(alpha) is determined by r(alpha) and the prior information.     

利用图像边缘变化特性寻找模糊处理痕迹

目前在图像认证技术中,一种不同于水印认证的盲检测技术发展迅速,这种认证技术不需要事先插入认证数据,只是根据图像本身的数据特征进行认证,其中对于图像处理痕迹的检测是盲检测的重要手段之一。本文提出了一种针对图像高斯模糊处理痕迹的检测算法,根据图像处理中高斯模糊的基本特性,首先对被检测原始图像采用不同的高斯半径进行二次模糊处理,然后用提出的算法来比较二次模糊后图像边缘的变化情况,算法可以从中发现原始图像的高斯模糊特性,从而发现原始图像中异常模糊处理的痕迹。仿真实验表明这种方法对于实现图像篡改的被动盲检测具有较好的应用价值。      

Variational image reconstruction from arbitrarily spaced samples: a fast multiresolution spline solution.

We propose a novel method for image reconstruction from nonuniform samples with no constraints on their locations. We adopt a variational approach where the reconstruction is formulated as the minimizer of a cost that is a weighted sum of two terms: (1) the sum of squared errors at the specified points and (2) a quadratic functional that penalizes the lack of smoothness. We search for a solution that is a uniform spline and show how it can be determined by solving a large, sparse system of linear equations. We interpret the solution of our approach as an approximation of the analytical solution that involves radial basis functions and demonstrate the computational advantages of our approach. Using the two-scale relation for B-splines, we derive an algebraic relation that links together the linear systems of equations specifying reconstructions at different levels of resolution. We use this relation to develop a fast multigrid algorithm. We demonstrate the effectiveness of our approach on some image reconstruction examples.     

一种基于二维拉格朗日连续水平集的图像分割方法

在图像分割领域中,基于离散水平集的图像分割方法不但对低信噪比图像难以实现正确地分割,而且分割速度较慢。针对这一问题,该文提出了一个基于连续水平集的图像分割方法。利用2维拉格朗日基函数的线性组合把水平集函数表示成连续函数。最小化实现图像分割的能量函数,建立基函数系数演化的微分方程。因此能量函数的最小值可直接根据拉格朗日的系数值获得。利用简单有限差分法对系数演化微分方程求解,实现了低信噪比图像的快速分割。实验结果表明该方法具有理想的分割结果。     

A back-projection autofocus algorithm based on flight trajectory optimization for synthetic aperture radar imaging

In this paper, a new autofocus algorithm is presented for back-projection (BP) image formation of synthetic aperture radar (SAR) imaging. The approach is based on maximizing a cost function obtained by prominent points in different sub-apertures of constructed SAR image by varying the flight trajectory parameters. While image-quality-based autofocus approach together with BP algorithm can be computationally intensive, we use approximations that allow optimal corrections to be derived. The approach is applicable for focusing different signal processing algorithms by obtaining modified flight trajectory parameters. Different examples demonstrate the effectiveness of the new autofocus approach applied to the frequency modulated continuous wave mode SAR dataset.     

A genetic algorithm approach to image sequence interpolation

Image sequence interpolation, or to obtain an up-sampled image sequence equivalently from a corresponding low-resolution image sequence, is an ill-posed inverse problem. In this study, three processing steps, namely, regularization, discretization and optimization, are used to convert the image sequence interpolation problem into a solvable optimization problem. In regularization, a fitness function combining a set of spatial and temporal performance measures for rating the quality of the interpolated (up-sampled) images is defined, which is used to convert the original ill-posed interpolation problem into a well-posed optimization problem. Discretization transforms the well-posed problem into a discrete one so that it can be solved numerically. Genetic algorithms (GAs) are used to optimize the solution in the discrete solution space using three basic operations, namely, reproduction, crossover and mutation. In the proposed approach, instead of only the spatial information within the current image frame employed in most existing methods, both the spatial and temporal information within the image sequence can be employed. Based on the experimental results obtained in this study, the interpolation results by the proposed approach are always better than those from the three existing approaches used for comparison. This shows the feasibility of the proposed approach.     

Seam carving based aesthetics enhancement for photos

Recently people are becoming more and more interested in the quality of photographs with the growing interest of image aesthetics. Many previous works start to focus on aesthetically enhancing the quality of images. In this paper, we come up with a novel approach to enhance image aesthetics. An aesthetically beautiful image usually has a clever composition of objects, the optimal positions of which have been deeply discussed by previous methods and reached good performance. After getting the optimal position of the object in images, we try to rearrange all the objects. Instead of picking the object out and pasting it on the suggested place, we propose an improved seam carving approach to change the relative positions of the objects in the image, which is able to move the object to a better place. We adopt the energy function to measure the saliency of each pixel and then find out the seams that should be cut off and inserted. After cutting off unimportant seams by pixel-removing and inserting seams by inpainting, we are able to maintain the resolution of the image as well as enhance the aesthetics in composition. In order to test the effectiveness of our method, we compare the performance of our approach with other state-of-the-art techniques, which well illustrates the satisfying performance of our method.     

Comment on “Robust blind image watermarking scheme based on Redundant Discrete Wavelet Transform and Singular Value Decomposition”

It is shown that the robust blind image watermarking scheme based on Redundant Discrete Wavelet Transform and Singular Value Decomposition proposed by Makbol and Khoo in this AEÜ journal has a fundamental flaw in its design that undermines the security of its scheme against the false-positive problem. More crucially, this result therefore invalidates the objective of the scheme that is to achieve ownership protection, so the scheme cannot be used. In more detail, an adversary is able to claim ownership of a watermarked image by extracting his watermark from the image, although his watermark has never been inserted into the image in the first place. We then verified the flaw through experimental results and thus, proved that the Makbol and Khoo scheme was not able to solve the false-positive problem as claimed and should not be used for proof of ownership applications. In discussing ways to mitigate the problem, we investigate an approach that would have worked quite efficiently if Discrete Wavelet Transform was used instead of Redundant Discrete Wavelet Transform.     

Original approach for the localisation of objects in images

An original approach is presented for the localisation of objects in an image which approach is neuronal and has two steps. In the first step, a rough localisation is performed by presenting each pixel with its neighbourhood to a neural net which is able to indicate whether this pixel and its neighbourhood are the image of the search object. This first filter does not discriminate for position. From its result, areas which might contain an image of the object can be selected. In the second step, these areas are presented to another neural net which can determine the exact position of the object in each area. This algorithm is applied to the problem of localising faces in images     

Peak transform for efficient image representation and coding.

In this work, we introduce a nonlinear geometric transform, called peak transform (PT), for efficient image representation and coding. The proposed PT is able to convert high-frequency signals into low-frequency ones, making them much easier to be compressed. Coupled with wavelet transform and subband decomposition, the PT is able to significantly reduce signal energy in high-frequency subbands and achieve a significant transform coding gain. This has important applications in efficient data representation and compression. To maximize the transform coding gain, we develop a dynamic programming solution for optimum PT design. Based on PT, we design an image encoder, called the PT encoder, for efficient image compression. Our extensive experimental results demonstrate that, in wavelet-based subband decomposition, the signal energy in high-frequency subbands can be reduced by up to 60% if a PT is applied. The PT image encoder outperforms state-of-the-art JPEG2000 and H.264 (INTRA) encoders by up to 2-3 dB in peak signal-to-noise ratio (PSNR), especially for images with a significant amount of high-frequency components. Our experimental results also show that the proposed PT is able to efficiently capture and preserve high-frequency image features (e.g., edges) and yields significantly improved visual quality. We believe that the concept explored in this work, designing a nonlinear transform to convert hard-to-compress signals into easy ones, is very useful. We hope this work would motivate more research work along this direction.     

A new approach in content-based image retrieval using fuzzy

Finding an image from a large set of images is an extremely difficult problem. One solution is to label images manually, but this is very expensive, time consuming and infeasible for many applications. Furthermore, the labeling process depends on the semantic accuracy in describing the image. Therefore many Content based Image Retrieval (CBIR) systems are developed to extract low-level features for describing the image content. However, this approach decreases the human interaction with the system due to the semantic gap between low-level features and high-level concepts. In this study we make use of fuzzy logic to improve CBIR by allowing users to express their requirements in words, the natural way of human communication. In our system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation. The texture and color attributes are computed in a way that model the Human Vision System (HSV). We proposed a new approach for graph matching that resemble the human thinking process. The proposed system is evaluated by different users with different perspectives and is found to match users’ satisfaction to a high degree.     

Quaternion structural similarity: a new quality index for color images

One of the most important issues for researchers developing image processing algorithms is image quality. Methodical quality evaluation, by showing images to several human observers, is slow, expensive, and highly subjective. On the other hand, a visual quality matrix (VQM) is a fast, cheap, and objective tool for evaluating image quality. Although most VQMs are good in predicting the quality of an image degraded by a single degradation, they poorly perform for a combination of two degradations. An example for such degradation is the color crosstalk (CTK) effect, which introduces blur with desaturation. CTK is expected to become a bigger issue in image quality as the industry moves toward smaller sensors. In this paper, we will develop a VQM that will be able to better evaluate the quality of an image degraded by a combined blur/desaturation degradation and perform as well as other VQMs on single degradations such as blur, compression, and noise. We show why standard scalar techniques are insufficient to measure a combined blur/desaturation degradation and explain why a vectorial approach is better suited. We introduce quaternion image processing (QIP), which is a true vectorial approach and has many uses in the fields of physics and engineering. Our new VQM is a vectorial expansion of structure similarity using QIP, which gave it its name-Quaternion Structural SIMilarity (QSSIM). We built a new database of a combined blur/desaturation degradation and conducted a quality survey with human subjects. An extensive comparison between QSSIM and other VQMs on several image quality databases-including our new database-shows the superiority of this new approach in predicting visual quality of color images.     

A novel algorithm of multi-sensor image fusion based on wavelet packet transform

In order to enhance the image information from multi-sensor and to improve the abilities of the information analysis and the feature extraction, this letter proposed a new fusion approach in pixel level by means of the Wavelet Packet Transform （WPT）. The WPT is able to decompose an image into low frequency band and high frequency band in higher scale. It offers a more precise method for image analysis than Wavelet Transform （WT）. Firstly, the proposed approach employs HIS （Hue, Intensity, Saturation） transform to obtain the intensity component of CBERS （China-Brazil Earth Resource Satellite） multi-spectral image. Then WPT transform is employed to decompose the intensity component and SPOT （Systeme Pour I＇Observation de la Therre ） image into low frequency band and high frequency band in three levels. Next, two high frequency coefficients and low frequency coefficients of the images are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approach can fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM （Histogram Matched）-based fusion algorithm and WT-based fusion approach.     

Rasterization theory,architectures, and implementations for a class of two-dimensional problems

This paper first presents a theory for rasterizing the class of two-dimensional problems which include signal/image processing, computer vision, and linear algebra. The rasterization theory is steered by an isomorphism relationship between the two-dimensional shuffle exchange network (2DSE) and the two-dimensional butterfly network (2DBN). Since in real-time applications, data are often acquired in a raster scan format, it is important to develop architectures to support the raster data structure. Algorithms are developed first by using 2DSE network, then transformed into 2DBN format. Rasterization architectures can be derived for the algorithms described by 2DBN format. In the PEACE project, we have been able to show that a single, fixed communication topology, namely 2DSE, provides solution times on a 2DSE parallel computing system that for many problems approach known theoretical lower bounds. Secondly, this paper presents the generic architectures and VLSI implementation examples for the rasterization structures.     

一种改进的退化文本图像二值化方法

文本图像二值化是文本图像识别的重要步骤,由于光照不均或文档水渍等原因导致文本图像退化,增加了文本图像识别的难度。本文对一种局部阈值算法进行了改进,首先对图像进行水平投影,根据直方图的极小点对版面进行简单划分,再利用全局阈值法估算出更为准确的各区域字符笔画宽度,从而自适应地得到适当的窗口尺寸,再利用对比图和局部阈值进行图像二值化,并结合OTSU图像消除原算法产生的伪轮廓。实验与分析表明,改进后的方法能够明显消除因笔画粗细不均、字符大小不同而产生的前景像素误识问题。     

A COVQ-based image coder for channels with bit errors and erasures

We illustrate how channel optimized vector quantization (COVQ) can be used for channels with both bit-errors and bit-erasures. First, a memoryless channel model is presented, and the performance of COVQ's trained for this channel is evaluated for an i.i.d. Gaussian source. Then, the new method is applied in implementing an error-robust sub-band image coder, and we present image results that illustrate the resulting performance. Our experiments show that the new approach is able to outperform a traditional scheme based on separate source and channel coding.